After a few months working in stealth mode, we are very proud to launch our Deep Learning initiative: luminoth.ai
Luminoth is an open source toolkit for computer vision. Currently, we support object detection and image classification, but we are aiming for much more. It is built in Python, using Google’s Machine Intelligence framework, TensorFlow; and Sonnet, a very useful library built by DeepMind for building complex neural networks with reusable components.
Over the last few years, the Machine Learning (ML) landscape has changed dramatically. The comeback of neural networks in the form of Deep Learning has opened new creative ways to approach many classical ML and Artificial Intelligence (AI) problems.
Out of all the areas vastly improved by Deep Learning techniques, computer vision has been particularly revolutionized by major breakthroughs that have only very recently occurred. In particular, there is a family of neural network architectures that have performed really well: Convolutional Neural Networks (CNNs or ConvNets, in ML lingo).
These networks have a number of properties that make them really well suited for image processing tasks. CNNs have the ability to spatially slide different filters through an image, and use stacks of these filters to recognize patterns of increasing level of abstractness, until they can grasp complex concepts that would otherwise be hard to express.
Deep neural networks also allow us to be very creative in terms of designing different architectures, by playing around with different layer types, trying out different configurations and experimenting with different hyperparameters. In fact, the state of the art of several of these technologies is rapidly changing almost in a everyday basis, due to this kind of fast prototyping aspect.
On the downside of these methods is the huge amount of labeled training data needed to get to meaningful results, which sometimes can be very expensive to get. As Machine Learning referent Andrew Ng states in many of his talks, this effect get specially aggravated in the case of Deep Learning.
By helping connect these developments to spawn new industry grade applications (which is our core expertise at Tryolabs), we believe there lies a massive opportunity. In fact, many of the improvements of incorporating ML derived technologies into our everyday lives will come from advancements in the field of computer vision.
As a few examples: self driving cars heavily depend on computer vision to understand their surroundings to make effective decisions. Augmented reality will be extremely benefited since apps will have to identify objects and scenarios. Image based medical diagnosis, video based security systems, satellite and drone imagery analysis, are only some of the countless possible applications of this emerging technologies.
Over the last couple of years, we have identified some issues most companies face when incorporating these technologies into their platforms:
Expect an intense learning curve for your team
SaaS solutions are important IP outside your platform, and are expensive on high usage scenarios.
Creating a functional implementations from a research paper, is damn hard
We are in the process of revealing and starting the Luminoth evangelization. This week (October 9th, 2017) we are traveling to London to start Luminoth international launch.
We’ll be giving talks on Google Campus London on Thursday 12th at noon and on Saturday 14th, we will be speaking at 11am on OSDC Europe, with that being our official release date. Talks will be given by Javier Rey, Tryolabs’ Lead researcher and Alan Descoins, our CTO.
Lastly, we’ll finish Luminoth launch tour speaking at MvdValley in November. Our talk will be about our experiences and lessons learned while launching international Machine Learning projects such as Luminoth and our spin-off company MonkeyLearn. We will also cover key techniques to sell Machine Learning related technologies internationally, particularly to the US markets focusing on Silicon Valley companies.
So, we have a very intense and fun ride ahead this month launching our new venture!
The Dark Visor is a Visor upgrade in Metroid Prime 2: Echoes. Designed by the Luminoth during the war, it was used by the Champion of Aether, A-Kul, to penetrate Dark Aether's haze in battle against the Ing.
We expect you to enjoy using Luminoth and make your life easier integrating Deep Learning based Computer Vision technologies into your company’s products. Feel free to explore our Github repository. All feedback and contributions are welcome. If you find this useful don’t forget to share and give Luminoth a star on Github! ⭐️
Update April 2018: We're announcing Luminoth 0.1! This new version brings several very exciting improvements, susch as the implementation of the SDD model and pretrained checkpoints. Read more about it in this blog post!